Youth Suicide in Florida
Andriy Koval
2019-11-21
Warning: package 'ggplot2' was built under R version 3.5.3Warning: package 'magrittr' was built under R version 3.5.3Warning: package 'dplyr' was built under R version 3.5.3Warning: package 'kableExtra' was built under R version 3.5.3I. Exposition
Glossary
Review object definitions to assist you in reading the report.
color_sex <- c(
"Male" = "blue"
,"Female" = "pink"
,"Total" = "black"
)
color_cause <- c(
"Firearms" = "salmon"
,"Other" = "green"
,"Total" = "black"
)
color_race <- c(
"black" = "grey"
,"blother" = "lightblue"
,"latino" = "yellow"
,"white" = "purple"
)
# utility-functions -------------------------------------------------------
quick_save <- function(g,name,...){
ggplot2::ggsave(
filename = paste0(name,".png"),
plot = g,
device = png,
path = "./analysis/talahassee/age12_18/prints/", # female marital educ poor_healt
# width = width,
# height = height,
# units = "cm",
# dpi = 50,
limitsize = FALSE,
...
)
}Review functions definitions to assist you in reading the report.
Load Data
ls_ds_wide <- readRDS("./data-unshared/derived/talahassee/12_18/ls_ds_wide.rds")
ls_ds_long <- readRDS("./data-unshared/derived/talahassee/12_18/ls_ds_long.rds")
ls_ds_long2 <- readRDS("./data-unshared/derived/talahassee/12_18/ls_ds_long2.rds")
ls_ds_long3 <- readRDS("./data-unshared/derived/talahassee/12_18/ls_ds_long3.rds")
ds_sex_cause <- ls_ds_long3$cause_sex
ds_sex_cause_race <- ls_ds_long3$cause_sex_race
ds_cause_sex <- ls_ds_long3$sex_cause
ds_cause_sex_race <- ls_ds_long3$sex_cause_raceData Tweaks
ds_sex %>% glimpse()Observations: 540
Variables: 6
$ race <chr> "allrace", "allrace", "allrace", "allrace", "allrace", "allrace", "allrace", "allrace", "al...
$ mortality_cause <chr> "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firear...
$ sex <chr> "Female", "Female", "Female", "Female", "Female", "Female", "Female", "Female", "Female", "...
$ year <int> 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2...
$ measure <fct> rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, r...
$ value <dbl> 0.1, 0.5, 0.4, 0.1, 0.5, 0.5, 0.9, 0.2, 0.5, 0.5, 1.4, 0.5, 1.4, 0.7, 0.6, 1.6, 3.6, 3.2, 2...ds_sex_race %>% glimpse()Observations: 2,160
Variables: 6
$ race <chr> "black", "black", "black", "black", "black", "black", "black", "black", "black", "black", "...
$ mortality_cause <chr> "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firear...
$ sex <chr> "Female", "Female", "Female", "Female", "Female", "Female", "Female", "Female", "Female", "...
$ year <int> 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2...
$ measure <fct> rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, rate, r...
$ value <dbl> 0.0, 0.0, 0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.0, 0.6, 0.0, 0.6, 0.0, 3.0, 2.4, 2...# g0 <- ds_sex %>%
# ggplot(aes(x = year, y = value, color = sex) )+
# geom_point()+
# geom_line(aes(group=sex))+
# # facet_grid(measure ~ ., scales = "free")+
# facet_grid(measure ~ mortality_cause, scales = "free")+
# theme_bw()
#
# g0 %>% print()
#
# g0 <- ls_ds_long4$sex_cause %>%
# ggplot(aes(x = year, y = value, color = mortality_cause) )+
# geom_point()+
# geom_line(aes(group=mortality_cause))+
# facet_grid(measure ~ sex, scales = "free")+
# theme_bw()
#
# g0 %>% print()
# Sonata form report structureds_sex_cause %>% glimpse()Observations: 105
Variables: 9
$ order <chr> "cause_sex", "cause_sex", "cause_sex", "cause_sex", "cause_sex", "cause_sex", "cause_sex",...
$ race <chr> "allrace", "allrace", "allrace", "allrace", "allrace", "allrace", "allrace", "allrace", "a...
$ mortality_cause <chr> "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firearms", "Firea...
$ sex <chr> "Female", "Female", "Female", "Female", "Female", "Female", "Female", "Female", "Female", ...
$ year <int> 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, ...
$ count <dbl> 1, 4, 3, 1, 4, 4, 7, 2, 4, 4, 11, 4, 11, 6, 5, 13, 30, 27, 18, 16, 29, 15, 21, 33, 25, 30,...
$ rate <dbl> 0.1, 0.5, 0.4, 0.1, 0.5, 0.5, 0.9, 0.2, 0.5, 0.5, 1.4, 0.5, 1.4, 0.7, 0.6, 1.6, 3.6, 3.2, ...
$ pct_change_count <dbl> NA, NA, NA, NA, NA, NA, -28.57143, NA, NA, NA, -54.54545, NA, -54.54545, -16.66667, 0.0000...
$ pct_change_rate <dbl> NA, NA, NA, NA, NA, NA, -33.333333, NA, NA, NA, -57.142857, NA, -57.142857, -14.285714, 0....II. Without Race
A1 - Sex-Cause
g1 <- ds_sex %>%
ggplot(aes(x = year, y = value, color = sex) )+
geom_point()+
geom_line(aes(group=sex))+
facet_grid(measure ~ mortality_cause, scales = "free")+
theme_bw()+
labs(
title = "Gender differences within each mortality cause"
)
g1 %>% print()Warning: Removed 20 rows containing missing values (geom_point).
A2 - Cause-Sex
g2 <- ds_sex %>%
ggplot(aes(x = year, y = value, color = mortality_cause) )+
geom_point()+
geom_line(aes(group=mortality_cause))+
facet_grid(measure ~ sex, scales = "free")+
theme_bw()+
labs(
title = "Mortality cause within each gender"
)
g2%>% print()Warning: Removed 20 rows containing missing values (geom_point).
III. With Race
B1 - Race-Sex-Cause
for(i in names(color_race) ){
g1 <- ds_sex_race %>%
dplyr::filter(race == i) %>%
ggplot(aes(x = year, y = value, color = sex) )+
geom_point()+
geom_line(aes(group=sex))+
facet_grid(measure ~ mortality_cause, scales = "free")+
theme_bw()+
labs(
title = paste0(toupper(i),": Gender differences within each mortality cause")
)
g1 %>% print()
}Warning: Removed 180 rows containing missing values (geom_point).Warning: Removed 2 rows containing missing values (geom_path).
Warning: Removed 136 rows containing missing values (geom_point).
Warning: Removed 148 rows containing missing values (geom_point).
Warning: Removed 26 rows containing missing values (geom_point).
B2 - Race-Cause-Sex
Warning: Removed 180 rows containing missing values (geom_point).
Warning: Removed 136 rows containing missing values (geom_point).
Warning: Removed 148 rows containing missing values (geom_point).
Warning: Removed 26 rows containing missing values (geom_point).
C1 - Sex-Cause-Race
for(i in names(color_sex) ){
g1 <- ds_sex_race %>%
dplyr::filter(sex == i) %>%
ggplot(aes(x = year, y = value, color = mortality_cause) )+
geom_point()+
geom_line(aes(group=mortality_cause))+
facet_grid(measure ~ race, scales = "free")+
theme_bw()+
labs(
title = paste0("Sex = ",toupper(i),": Mortality causes for each race")
)
g1 %>% print()
}Warning: Removed 130 rows containing missing values (geom_point).
Warning: Removed 274 rows containing missing values (geom_point).Warning: Removed 2 rows containing missing values (geom_path).
Warning: Removed 86 rows containing missing values (geom_point).
C2 - Sex-Race-Cause
for(i in names(color_sex) ){
g1 <- ds_sex_race %>%
dplyr::filter(sex == i) %>%
ggplot(aes(x = year, y = value, color = race) )+
geom_point()+
geom_line(aes(group=race))+
facet_grid(measure ~ mortality_cause , scales = "free")+
theme_bw()+
labs(
title = paste0("Sex = ",toupper(i),": Race differences for each mortality cause")
)
g1 %>% print()
}Warning: Removed 130 rows containing missing values (geom_point).
Warning: Removed 274 rows containing missing values (geom_point).Warning: Removed 2 rows containing missing values (geom_path).
Warning: Removed 86 rows containing missing values (geom_point).
D1 - Cause-Sex-Race
for( i in names(color_cause) ){
g1 <- ds_sex_race %>%
dplyr::filter(mortality_cause == i) %>%
ggplot(aes(x = year, y = value, color = sex) )+
geom_point()+
geom_line(aes(group=sex))+
facet_grid(measure ~ race, scales = "free")+
theme_bw()+
labs(
title = paste0("Cause = ",toupper(i),": Gender differences in each race")
)
g1 %>% print()
}Warning: Removed 234 rows containing missing values (geom_point).Warning: Removed 2 rows containing missing values (geom_path).
Warning: Removed 158 rows containing missing values (geom_point).
Warning: Removed 98 rows containing missing values (geom_point).
D2 - Cause-Race-Sex
for( i in names(color_cause) ){
g1 <- ds_sex_race %>%
dplyr::filter(mortality_cause == i) %>%
ggplot(aes(x = year, y = value, color = race) )+
geom_point()+
geom_line(aes(group=race))+
facet_grid(measure ~ sex, scales = "free")+
theme_bw()+
labs(
title = paste0("Cause = ",toupper(i),": Race differences for each gender")
)
g1 %>% print()
}Warning: Removed 234 rows containing missing values (geom_point).
Warning: Removed 158 rows containing missing values (geom_point).
Warning: Removed 98 rows containing missing values (geom_point).
IV. Recap
Report rendered by an499583 at 2019-11-21, 10:14 -0500R version 3.5.2 (2018-12-20)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-2 dichromat_2.0-0 kableExtra_1.1.0 dplyr_0.8.3 magrittr_1.5 ggplot2_3.1.1
[7] knitr_1.26
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 pillar_1.4.2 compiler_3.5.2 plyr_1.8.4 tools_3.5.2 zeallot_0.1.0
[7] digest_0.6.22 lifecycle_0.1.0 viridisLite_0.3.0 evaluate_0.14 tibble_2.1.3 gtable_0.3.0
[13] pkgconfig_2.0.3 rlang_0.4.1 cli_1.1.0 rstudioapi_0.10 yaml_2.2.0 xfun_0.11
[19] xml2_1.2.2 httr_1.4.1 withr_2.1.2 stringr_1.4.0 vctrs_0.2.0 hms_0.5.2
[25] webshot_0.5.1 tidyselect_0.2.5 glue_1.3.1 R6_2.4.1 fansi_0.4.0 rmarkdown_1.17
[31] reshape2_1.4.3 tidyr_1.0.0 purrr_0.3.3 readr_1.3.1 ellipsis_0.3.0 scales_1.0.0
[37] backports_1.1.5 htmltools_0.4.0 rvest_0.3.5 assertthat_0.2.1 colorspace_1.4-1 labeling_0.3
[43] utf8_1.1.4 stringi_1.4.3 lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4